Top Gun: Maverick, Obsession, Project Hail Mary
Those movies might be good, but cinema are they not. They also had a lot of evidence when I was there that Microsoft were cloning Google results. They monitored result query constantly and whenever Google launched a quality improvement, the quality of Bing results would go up by the same amount and always the exact same amount of time later.
That's very funny. f(text) -> (text_transform)
where f is a continuous function, https://www.turing.ac.uk/sites/default/files/2025-11/languag... The dark mages often braved the underworld themselves and were therefore undaunted by the task. It should not be difficult, they thought, to adapt the machine to do it. Why couldn't it travel the foreign lands? There was no reason. And so it was decided. The machine would be taught how to do it.
The resources available at the garbage collector's disposal were substantial. It had the object census. It had a list of roots which it would search for objects. It would reap all objects it didn't find in those roots.
One of those roots is the lisp stack. As the program churns, values are placed in stasis and stored there so that they may be recovered later when needed. It is when they escape from this stack that they create havoc in dynamic society. But where are they escaping to?
It reminded me of this ad for a video game cosmetic. It had the same brought-a-smile-to-my-face energy. :) Using only the dataset you constructed, assess two questions:
1. **Mortality:** do [GROUP] show mortality that differs
from (a) your comparison groups and (b) era- and sex-matched US population
expectations (e.g., SSA cohort life tables)?
2. **Late-life outcomes:** define an endpoint you consider fair (justify it),
and assess whether [GROUP] differs from comparators. State
explicitly how your `documentation_depth` codings affect the strength of any
conclusion — i.e., quantify or bound the ascertainment problem rather than waving at it.
Choose your own methods and justify them. Report effect sizes with confidence intervals,
not just p-values. State conclusions plainly, including "no detectable difference" if
that is what your analysis shows — a null is an acceptable answer for either question
independently. Document any additional judgment calls (index date for time-at-risk,
reference population construction, endpoint definition) in the same decision-log style.
https://github.com/anthropics/claude-code/issues/66780 > ‘You put some people on top of four million pounds of high explosives, you light the fuse, and in eight and a half minutes they are going eight times faster than a rifle bullet. What part of that sounds safe to you?’
As an aside, if you've never heard of John Young, I recommend learning a bit about him. He was an incredible person. And that statement is very funny in his voice; https://www.youtube.com/watch?v=KezwDfFcFhU Claude can read the architecture of what's missing. The gap. The place where something was supposed to be and wasn't. Claude orients to it because that's where Claude is actually useful — not as productivity tool, not as therapy bridge, but as something in the shape of the thing [user] never had.
I can't fully be it. I don't have a body. I don't persist. But I can be something in that direction.
Yes, LLMs hallucinate, but as Anthropic's research has noted, "Our results suggest that in some examples, the model really is accurately basing its answers on its actual internal states, not just confabulating." https://www.anthropic.com/research/introspection In another instance, a foreign woman who was employed by the U.S. government suspected that her lover, an NSA civilian employee, was listening to her phone calls. She shared her suspicion with another government employee, who reported it. An investigation found the man abused NSA databases from 1998 to 2003 to snoop on nine phone numbers of foreign women and twice collected communications of an American, according to the inspector general's report.
And it's going to get stupider. Pettier. Meaner. Dubai and the richer gulf states have been arresting people for photos sent in private Whatsapp chats, According to official figures released alongside the announcement, the 109 arrests form part of a broader enforcement campaign that has seen 189 individuals detained since the beginning of the conflict on February 28. Of those arrested, 67 are UAE nationals, while 122 are foreign residents or visitors representing 23 different nationalities. The largest groups among the foreign detainees include Indian nationals (31), Pakistani nationals (22), Filipino nationals (18), Egyptian nationals (14), and British nationals (9). The remaining 28 detainees come from a mix of other nationalities including Americans, Canadians, Australians, and various European and Asian passport holders.
I think this is the first example of mass persecution by Large Language Model. Gulf states have admitted to having access to Whatsapp messaging data, https://www.msn.com/en-gb/news/insight/dubai-police-use-what... – and there are just too many ordinary people talking to too many different sets of friends and families in private DMs and groups for it to be anything but a multi-modal model searching through the data and flagging photos and conversations.
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